Effectiveness of Odor-Baited Trap Trees for Plum Curculio (Coleoptera: Curculionidae) Monitoring in Commercial Apple Orchards in the Northeast
Bibliographic record
Abstract
The plum curculio, Conotrachelus nenuphar (Herbst), is a key pest of pome and stone fruit in eastern and central North America. For effective management of this insect pest in commercial apple (Malus spp.) orchards in the northeastern United States and Canada, one of the greatest challenges has been to determine the need for and timing of insecticide applications that will protect apple fruit from injury by adults. In a 2004-2005 study, we assessed the efficacy and economic viability of a reduced-risk integrated pest management strategy involving an odor-baited trap tree approach to determine need for and timing of insecticide use against plum curculio based on appearance of fresh egg-laying scars. Evaluations took place in commercial apple orchards in seven northeastern U.S. states. More specifically, we compared the trap-tree approach with three calendar-driven whole-block sprays and with heat-unit accumulation models that predict how long insecticide should be applied to orchard trees to prevent injury by plum curculio late in the season. Trap tree plots received a whole-plot insecticide spray by the time of petal fall, and succeeding sprays (if needed) were applied to peripheral-row trees only, depending on a threshold of one fresh plum curculio egg-laying scar out of 25 fruit sampled from a single trap tree. In both years, level of plum curculio injury to fruit sampled from perimeter-row, the most interior-row trees and whole-plot injury in trap tree plots did not differ significantly from that recorded in plots subject to conventional management or in plots managed using the heat-unit accumulation approach. The amount of insecticide used in trap tree plots was reduced at least by 43% compared with plots managed with the conventional approach. Advantages and potential pitfalls of the bio-based trap tree approach to plum curculio monitoring in apple orchards are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".